ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2/W4, 43-50, 2017
https://doi.org/10.5194/isprs-annals-IV-2-W4-43-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
 
12 Sep 2017
USING MULTI-SCALE FEATURES FOR THE 3D SEMANTIC LABELING OF AIRBORNE LASER SCANNING DATA
R. Blomley and M. Weinmann Institute of Photogrammetry and Remote Sensing, Karlsruhe Institute of Technology (KIT), Englerstr. 7, 76131 Karlsruhe, Germany
Keywords: 3D Semantic Labeling, Airborne Laser Scanning, Point Cloud, Multi-Scale Features, Classification Abstract. In this paper, we present a novel framework for the semantic labeling of airborne laser scanning data on a per-point basis. Our framework uses collections of spherical and cylindrical neighborhoods for deriving a multi-scale representation for each point of the point cloud. Additionally, spatial bins are used to approximate the topography of the considered scene and thus obtain normalized heights. As the derived features are related with different units and a different range of values, they are first normalized and then provided as input to a standard Random Forest classifier. To demonstrate the performance of our framework, we present the results achieved on two commonly used benchmark datasets, namely the Vaihingen Dataset and the GML Dataset A, and we compare the results to the ones presented in related investigations. The derived results clearly reveal that our framework excells in classifying the different classes in terms of pointwise classification and thus also represents a significant achievement for a subsequent spatial regularization.
Conference paper (PDF, 1378 KB)


Citation: Blomley, R. and Weinmann, M.: USING MULTI-SCALE FEATURES FOR THE 3D SEMANTIC LABELING OF AIRBORNE LASER SCANNING DATA, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2/W4, 43-50, https://doi.org/10.5194/isprs-annals-IV-2-W4-43-2017, 2017.

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